Instructions to use ydshieh/tiny-random-ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ydshieh/tiny-random-ViTForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ydshieh/tiny-random-ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("ydshieh/tiny-random-ViTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ydshieh/tiny-random-ViTForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 292 kB
-
https://huggingface.co/ydshieh/tiny-random-ViTForImageClassification/resolve/main/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-ViTForImageClassification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-ViTForImageClassification/resolve/main/tf_model.h5
292 kB
- Xet hash:
- 47cb3c5eccaa5fbd38d2d087374ad8d4e0b109031e11ff09fe3c2e62321f5844
- Size of remote file:
- 292 kB
- SHA256:
- 76d9108ebd5cb2ea557defc51f121d09b1d91a2c94216f0a02eb9338ae1ea338
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